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Personalized Virus Load Curves for Acute Viral Infections.

Carlos Contreras1,2, Jay M Newby1,2, Thomas Hillen1,2

  • 1Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB T6G 2R3, Canada.

Viruses
|September 28, 2021
PubMed
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We developed a new explicit function for patient-specific virus load curves in acute viral infections. This model simplifies analysis and accurately fits influenza and SARS-CoV-2 data, showing individual disease variability.

Area of Science:

  • Virology
  • Mathematical Biology
  • Infectious Disease Modeling

Background:

  • Accurate modeling of virus load dynamics is crucial for understanding acute viral infections.
  • Existing models often require complex computations, limiting patient-specific analysis.
  • Individual variability in disease progression necessitates adaptable modeling approaches.

Purpose of the Study:

  • To introduce a novel, explicit function for describing patient-specific virus load curves.
  • To enable simplified analysis of acute viral infections without complex dynamic models.
  • To provide a new tool for estimating infection growth rates and assessing patient risk.

Main Methods:

  • Developed an explicit function based on intuitive model parameters for virus load.
Keywords:
SARS-CoV-2mathematical modelingpatient specificviral load

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  • Validated the function using diverse datasets: influenza A (mice and human), human rhinovirus, and SARS-CoV-2 (monkey and human).
  • Compared the explicit function against an established target model and exponential approximations.

    • Main Results:

    • The explicit function accurately describes patient-specific virus load curves across multiple viral infections.
    • Wide parameter distributions indicate significant inter-individual variability in disease outcomes.
    • The function, target model, and approximations demonstrated excellent data fits.

    Conclusions:

    • The new virus load function provides an effective method for analyzing patient-specific viral data.
    • It can be integrated into higher-level models for physiological effects, tissue damage, and risk assessment.
    • This approach simplifies virus load analysis and accounts for individual patient differences.